Resource Rational Contractualism Should Guide AI Alignment

Authors

  • Sydney Levine Harvard University Massachusetts Institute of Technology
  • Matija Franklin Google Deepmind
  • Tan Zhi-Xuan Massachusetts Institute of Technology
  • Secil Yanik Guyot Australian National University
  • Julia Haas Google Deepmind
  • Lionel Wong Stanford University
  • Daniel Kilov Australian National University
  • Yejin Choi Stanford University
  • Joshua B. Tenenbaum Massachusetts Institute of Technology
  • Noah Goodman Google Deepmind
  • Seth Lazar Australian National University
  • Iason Gabriel Google Deepmind

Abstract

AI systems will soon have to navigate human environments and make decisions that affect people and other AI agents whose goals and values diverge. Contractualist alignment proposes grounding those decisions in agreements that diverse stakeholders would endorse under the right conditions, yet securing such agreement at scale remains costly and slow—even for advanced AI. We therefore proposeResource-Rational Contractualism (RRC): a framework where AI systems approximate the agreements rational parties would form by drawing on a toolbox of normatively-grounded, cognitively-inspired heuristics that trade effort for accuracy. An RRC-aligned agent would not only operate efficiently, but also be equipped to dynamically adapt to and interpret the ever-changing human social world.

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Published

2026-07-15

How to Cite

Levine, S., Franklin, M., Zhi-Xuan, T., Yanik Guyot, S., Haas, J., Wong, L., … Gabriel, I. (2026). Resource Rational Contractualism Should Guide AI Alignment. Proceedings of IASEAI Conference, 2(1), 353–368. Retrieved from https://ojs.aaai.org/index.php/IASEAI/article/view/43037